Selecting imaging perspectives to optimize tracking volume detectability and model quality

ABSTRACT

A method is described including online selecting of at least one of a plurality of optimal imaging times or a plurality of imaging angles and optimizing a parameter of an imaging system based on the online selecting.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/014,021, filed Sep. 8, 2020, which is hereby incorporated byreference.

TECHNICAL FIELD

The present disclosure relates to selecting imaging perspectives tooptimize tracking volume detectability and model quality.

BACKGROUND

In radiation treatment, a radiation delivery system may utilize motiontracking to determine a correlation between a motion of an intendedtarget or region of interest and a direct measurement of a position of atracking structure. The correlation is determined by fitting a motionmodel that registers the target or region of interest and predicts themotion relative to the tracking structure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousimplementations of the disclosure.

FIG. 1A illustrates a helical radiation delivery system, in accordancewith embodiments described herein.

FIG. 1B illustrates a robotic radiation treatment system that may beused in accordance with embodiments described herein.

FIG. 1C illustrates a C-arm gantry-based radiation treatment system, inaccordance with embodiments described herein.

FIG. 2 depicts a flow diagram of a method of selecting imagingperspectives to optimize tracking volume detectability and modelquality, in accordance with embodiments of the disclosure.

FIG. 3A is an illustration of an example of an imaging system havingmultiple perspectives of a region of interest, in accordance withembodiments of the disclosure.

FIG. 3B is an illustration of an example of selecting a perspective forcapturing a subsequent image based on tracking quality metrics, inaccordance with embodiments of the disclosure.

FIG. 4 depicts a flow diagram of a method of updating a model based onreceiving a subsequent image of a region of interest, in accordance withembodiments of the disclosure.

FIG. 5 is a block diagram of an example computing device that mayperform one or more of the operations described herein, in accordancewith some embodiments.

DETAILED DESCRIPTION

Described herein are embodiments of methods and apparatus for selectingimaging perspectives to optimize tracking volume detectability and modelquality. A radiation delivery system may utilize high-frequency onlinemotion tracking. The high-frequency online motion tracking depends upona correlation between the motion of an intended target and alow-frequency but accurate and direct measurement (for example, atwo-dimensional (2D) x-ray image) of the position of a trackingstructure (assumed to be moving in tandem with the target) and a highfrequency motion surrogate (for example, an LED marker trackingrespiratory or other quasi-periodic motion or nothing under a statictarget assumption).

To acquire the low-frequency direct measurements, the radiation deliverysystem may include an imaging system, such as a kilovolt (kV) ormagnetic resonance (MR) imaging system, to capture 2D x-ray images of aregion of interest (ROI) that may include the tracking structure.

When acquiring these images, there is a tradeoff between the amount ofnon-therapeutic imaging dose (and/or imaging time/frequency) andposition uncertainty. For example, taking a smaller number of imagesreduces the non-therapeutic imaging dose that a patient is exposed to,but increases the position uncertainty (also referred to as“under-sampling” hereafter). Conversely, taking a larger number ofimages reduces the position uncertainty, but increases thenon-therapeutic imaging dose that the patient is exposed to (alsoreferred to as “over-sampling” hereafter). Accordingly, it is importantto balance both of these considerations when determining what timesthese images are to be captured at and/or what perspectives these imagesare to be captured from.

Aspects of the disclosure may remedy the above and other deficiencies byselecting imaging perspectives and/or times that optimize trackingvolume and model quality. When an image of an ROI is acquired from afirst perspective, the image may have an uncertainty value thatcorresponds to a position uncertainty associated with the ROI. Forexample, the uncertainty value may correspond to a position uncertaintyof a tracking structure or a target within the ROI.

Processing logic of a processing device may generate a model associatedwith the ROI based on the image. The model may include on or moreparameters associated with the ROI. In embodiments, the one or moreparameters may be any characteristics or values that may influence theposition uncertainty of the ROI. Examples of parameters associated withthe ROI may include, but are not limited to, a respiratory motiontrajectory associated with the ROI, radiation sensitive structureswithin the ROI, visibility of the ROI, or a perspective of the ROI.

The processing logic may use the model to determine tracking qualitymetrics for multiple perspectives that a subsequent image may becaptured from of the ROI. The tracking quality metrics may indicate areduction of the uncertainty value associated with the ROI by asubsequent image that would be captured from each of the perspectives.The tracking quality metrics may be used to identify one or moreperspectives that have high reductions of the uncertainty value relativeto other perspectives.

The processing logic may select a second perspective from the multipleperspectives for the capturing of a subsequent image based on thetracking quality metrics. The processing logic may select the secondperspective based on a corresponding tracking quality metric indicatingthat the subsequent image from the second perspective would reduce theuncertainty value associated with the ROI. Selecting the secondperspective ensures that the subsequent image will decrease the positionuncertainty of the ROI, preventing the capturing of subsequent imagesthat may not contain useful information (e.g., do not improve positionuncertainty of the ROI) and reducing or eliminating over-sampling. Byreducing or eliminating over-sampling, the non-therapeutic imaging dosethat a patient may be exposed to is also reduced, improving theperformance of the radiation delivery system.

Although embodiments of the disclosure are described in the context of aradiation delivery system, such descriptions are provided forillustrative purposes only. Aspects of the disclosure may be utilized byany type of imaging system that would benefit from the optimization ofselection of imaging perspectives of a ROI. For example, aspects of thedisclosure may be utilized by various types of surgical guidance systemsthat include imaging systems. Furthermore, although embodiments of thedisclosure may be described utilizing a kV imaging system, aspects ofthe disclosure may be utilized by other types of imaging systems, suchas MR imaging systems or megavolt (MV) imaging systems.

In embodiments, aspects of the disclosure may provide for an improved MRimaging system. Because MR imaging does not involve a radiation dose,reducing a patient's exposure to radiation is not required whenutilizing an MR imaging system. However, aspects of the disclosure maybe used to optimize the position, orientation, and/or timing of imagesthat are acquired of an ROI using an MR imaging system. For example, MRimaging systems may be unable to capture a three-dimensional (3D) imagequickly enough to capture motion across an entire ROI. Rather thanattempting to capture a 3D image of the ROI, multiple one-dimensional(1D) or 2D MR images (which can be acquired more quickly than a 3Dimage) may be acquired from a variety of positions and orientationswithin the ROI. Aspects of the disclosure may be utilized to optimizethe position, orientation, and/or timing of these images to minimize theuncertainty associated with tracking key targets and sensitivestructures within the ROI using an MR imaging system.

FIG. 1A illustrates a helical radiation delivery system 800 inaccordance with embodiments of the present disclosure. The helicalradiation delivery system 800 may include a linear accelerator (LINAC)850 mounted to a ring gantry 820. The LINAC 850 may be used to generatea radiation beam (i.e., treatment beam) by directing an electron beamtowards an x-ray emitting target. The treatment beam may deliverradiation to a target region (i.e., a tumor). The treatment systemfurther includes a multileaf collimator (MLC) 860 coupled with thedistal end of the LINAC 850. The MLC includes a housing that housesmultiple leaves that are movable to adjust an aperture of the MLC toenable shaping of the treatment beam. In embodiments, the MLC 860 may bea binary MLC that includes a plurality of leaves arranged in twoopposing banks, where the leaves of the two opposing banks areinterdigitated with one another and can be opened or closed to form anaperture. In some embodiments, the MLC 860 may be anelectromagnetically-actuated MLC. In embodiments, MLC 860 may be anyother type of MHLC. The ring gantry 820 has a toroidal shape in whichthe patient 830 extends through a bore of the ring/toroid and the LINAC850 is mounted on the perimeter of the ring and rotates about the axispassing through the center to irradiate a target region with beamsdelivered from one or more angles around the patient. During treatment,the patient 830 may be simultaneously moved through the bore of thegantry on a treatment couch 840.

The helical radiation delivery system 800 includes an imaging system,comprising the LINAC 850 as an imaging source and an x-ray detector 870.The LINAC 850 may be used to generate a mega-voltage x-ray image (MVCT)of a region of interest (ROI) of patient 830 by directing a sequence ofx-ray beams at the ROI which are incident on the x-ray detector 870opposite the LINAC 850 to image the patient 830 for setup and generatepre-treatment images. In one embodiment, the helical radiation deliverysystem 800 may also include a secondary imaging system consisting of akV imaging source 810 mounted orthogonally relative to the LINAC 850(e.g., separated by 90 degrees) on the ring gantry 820 and may bealigned to project an imaging x-ray beam at a target region and toilluminate an imaging plane of a detector after passing through thepatient 130.

FIG. 1B illustrates a radiation treatment system 1200 that may be usedin accordance with alternative embodiments described herein. As shown,FIG. 1B illustrates a configuration of a radiation treatment system1200. In the illustrated embodiments, the radiation treatment system1200 includes a linear accelerator (LINAC) 1201 that acts as a radiationtreatment source and an MLC 1205 coupled with the distal end of theLINAC 1201 to shape the treatment beam. In one embodiment, the LINAC1201 is mounted on the end of a robotic arm 1202 having multiple (e.g.,5 or more) degrees of freedom in order to position the LINAC 1201 toirradiate a pathological anatomy (e.g., target) with beams deliveredfrom many angles, in many planes, in an operating volume around apatient. Treatment may involve beam paths with a single isocenter,multiple isocenters, or with a non-isocentric approach.

LINAC 1201 may be positioned at multiple different nodes (predefinedpositions at which the LINAC 1201 is stopped and radiation may bedelivered) during treatment by moving the robotic arm 1202. At thenodes, the LINAC 1201 can deliver one or more radiation treatment beamsto a target, where the radiation beam shape is determined by the leafpositions in the MLC 1205. The nodes may be arranged in an approximatelyspherical distribution about a patient. The particular number of nodesand the number of treatment beams applied at each node may vary as afunction of the location and type of pathological anatomy to be treated.

In another embodiment, the robotic arm 1202 and LINAC 1201 at its endmay be in continuous motion between nodes while radiation is beingdelivered. The radiation beam shape and 2-D intensity map is determinedby rapid motion of the leaves in the MLC 1205 during the continuousmotion of the LINAC 1201.

The radiation treatment system 1200 includes an imaging system 1210having a processing device 1230 connected with x-ray sources 1203A and1203B (i.e., imaging sources) and fixed x-ray detectors 1204A and 1204B.Alternatively, the x-ray sources 1203A, 1203B and/or x-ray detectors1204A, 1204B may be mobile, in which case they may be repositioned tomaintain alignment with the target, or alternatively to image the targetfrom different orientations or to acquire many x-ray images andreconstruct a three-dimensional (3D) cone-beam CT. In one embodiment,the x-ray sources are not point sources, but rather x-ray source arrays,as would be appreciated by the skilled artisan. In one embodiment, LINAC1201 serves as an imaging source, where the LINAC power level is reducedto acceptable levels for imaging.

Imaging system 1210 may perform computed tomography (CT) such as conebeam CT or helical megavoltage computed tomography (MVCT), and imagesgenerated by imaging system 1210 may be two-dimensional (2D) orthree-dimensional (3D). The two x-ray sources 1203A and 1203B may bemounted in fixed positions on the ceiling of an operating room and maybe aligned to project x-ray imaging beams from two different angularpositions (e.g., separated by 90 degrees) to intersect at a machineisocenter (referred to herein as a treatment center, which provides areference point for positioning the patient on a treatment couch 1206during treatment) and to illuminate imaging planes of respectivedetectors 1204A and 1204B after passing through the patient. In oneembodiment, imaging system 1210 provides stereoscopic imaging of atarget and the surrounding volume of interest (VOI). In otherembodiments, imaging system 1210 may include more or less than two x-raysources and more or less than two detectors, and any of the detectorsmay be movable rather than fixed. In yet other embodiments, thepositions of the x-ray sources and the detectors may be interchanged.Detectors 1204A and 1204B may be fabricated from a scintillatingmaterial that converts the x-rays to visible light (e.g., amorphoussilicon), and an array of CMOS (complementary metal oxide silicon) orCCD (charge-coupled device) imaging cells that convert the light to adigital image that can be compared with a reference image during animage registration process that transforms a coordinate system of thedigital image to a coordinate system of the reference image, as is wellknown to the skilled artisan. The reference image may be, for example, adigitally reconstructed radiograph (DRR), which is a virtual x-ray imagethat is generated from a 3D CT image based on simulating the x-ray imageformation process by casting rays through the CT image.

In one embodiment, IGRT delivery system 1200 also includes a secondaryimaging system 1239. Imaging system 1239 is a Cone Beam ComputedTomography (CBCT) imaging system, for example, the medPhoton ImagingRingSystem. Alternatively, other types of volumetric imaging systems may beused. The secondary imaging system 1239 includes a rotatable gantry 1240(e.g., a ring) attached to an arm and rail system (not shown) that movethe rotatable gantry 1240 along one or more axes (e.g., along an axisthat extends from a head to a foot of the treatment couch 1206. Animaging source 1245 and a detector 1250 are mounted to the rotatablegantry 1240. The rotatable gantry 1240 may rotate 360 degrees about theaxis that extends from the head to the foot of the treatment couch.Accordingly, the imaging source 1245 and detector 1250 may be positionedat numerous different angles. In one embodiment, the imaging source 1245is an x-ray source and the detector 1250 is an x-ray detector. In oneembodiment, the secondary imaging system 1239 includes two rings thatare separately rotatable. The imaging source 1245 may be mounted to afirst ring and the detector 1250 may be mounted to a second ring. In oneembodiment, the rotatable gantry 1240 rests at a foot of the treatmentcouch during radiation treatment delivery to avoid collisions with therobotic arm 1202.

As shown in FIG. 1B, the image-guided radiation treatment system 1200may further be associated with a treatment delivery workstation 150. Thetreatment delivery workstation may be remotely located from theradiation treatment system 1200 in a different room than the treatmentroom in which the radiation treatment system 1200 and patient arelocated. The treatment delivery workstation 150 may include a processingdevice (which may be processing device 1230 or another processingdevice) and memory that modify a treatment delivery to the patient 1225based on a detection of a target motion that is based on one or moreimage registrations, as described herein.

FIG. 1C illustrates a C-arm radiation delivery system 1400. In oneembodiment, in the C-arm system 1400 the beam energy of a LINAC may beadjusted during treatment and may allow the LINAC to be used for bothx-ray imaging and radiation treatment. In another embodiment, the system1400 may include an onboard kV imaging system to generate x-ray imagesand a separate LINAC to generate the higher energy therapeutic radiationbeams. The system 1400 includes a C-arm gantry 1410, a LINAC 1420, anMLC 1470 coupled with the distal end of the LINAC 1420 to shape thebeam, and a portal imaging detector 1450. The C-arm gantry 1410 may berotated to an angle corresponding to a selected projection and used toacquire an x-ray image of a VOI of a patient 1430 on a treatment couch1440. In embodiments that include a portal imaging system, the LINAC1420 may generate an x-ray beam that passes through the target of thepatient 1430 and are incident on the portal imaging detector 1450,creating an x-ray image of the target. After the x-ray image of thetarget has been generated, the beam energy of the LINAC 1420 may beincreased so the LINAC 1420 may generate a radiation beam to treat atarget region of the patient 1430. In another embodiment, the kV imagingsystem may generate an x-ray beam that passes through the target of thepatient 1430, creating an x-ray image of the target. In someembodiments, the portal imaging system may acquire portal images duringthe delivery of a treatment. The portal imaging detector 1450 maymeasure the exit radiation fluence after the beam passes through thepatient 1430. This may enable internal or external fiducials or piecesof anatomy (e.g., a tumor or bone) to be localized within the portalimages.

Alternatively, the kV imaging source or portal imager and methods ofoperations described herein may be used with yet other types ofgantry-based systems. In some gantry-based systems, the gantry rotatesthe kV imaging source and LINAC around an axis passing through theisocenter. Gantry-based systems include ring gantries having generallytoroidal shapes in which the patient's body extends through the bore ofthe ring/toroid, and the kV imaging source and LINAC are mounted on theperimeter of the ring and rotates about the axis passing through theisocenter. Gantry-based systems may further include C-arm gantries, inwhich the kV imaging source and LINAC are mounted, in a cantilever-likemanner, over and rotates about the axis passing through the isocenter.In another embodiment, the kV imaging source and LINAC may be used in arobotic arm-based system, which includes a robotic arm to which the kVimaging source and LINAC are mounted as discussed above. Aspects of thepresent disclosure may further be used in other such systems such as agantry-based LINAC system, static imaging systems associated withradiation therapy and radiosurgery, proton therapy systems using anintegrated image guidance, interventional radiology and intraoperativex-ray imaging systems, etc.

FIG. 2 depicts a flow diagram of a method 200 of selecting imagingperspectives to optimize tracking volume detectability and modelquality, in accordance with embodiments of the disclosure. Method 200may be performed by processing logic that may comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, a processor, aprocessing device, a central processing unit (CPU), a system-on-chip(SoC), etc.), software (e.g., instructions running/executing on aprocessing device), firmware (e.g., microcode), or a combinationthereof. In embodiments, various portions of method 200 may be performedby processing logic of a processing device of a radiation deliverysystem as previously described at FIGS. 1A-1C.

With reference to FIG. 2 , method 200 illustrates example functions usedby various embodiments. Although specific function blocks (“blocks”) aredisclosed in method 200, such blocks are examples. That is, embodimentsare well suited to performing various other blocks or variations of theblocks recited in method 200. It is appreciated that the blocks inmethod 200 may be performed in an order different than presented, andthat not all of the blocks in method 200 may be performed.

Method 200 begins at block 210, where the processing logic identifies animage of an ROI from a first perspective that has an uncertainty valueassociated with the ROI. In embodiments, the uncertainty value maycorrespond to a position uncertainty associated with the ROI and/or oneor more objects within the ROI. For example, the uncertainty value maycorrespond to a position uncertainty associated with tracking structurewithin the ROI.

In some embodiments, the image of the ROI may be acquired by an imagingsystem during a treatment session being administered by a radiationdelivery system. In embodiments, the image of the ROI may be apreviously acquired image. For example, the image may be an image of theROI that was previously captured as part of a treatment planningsession.

At block 220, the processing logic generates a model that includes oneor more parameters associated with the ROI based on the image. The oneor more parameters may be any characteristics or values that mayinfluence the position uncertainty of the ROI. In embodiments, theparameters may include a respiratory motion trajectory of a patientassociated with the ROI. The respiratory motion trajectory may describethe motion of the ROI during different phases of the patient'srespiratory cycle and include the timing of the various phases of therespiratory cycle. In embodiments, the parameters may include radiationsensitive structures within the ROI or in proximity to the ROI. Forexample, the parameters may indicate the rectum of a patient is withinthe ROI or is in proximity to the ROI. In an embodiment, the parametersmay include a visibility of the ROI by the imaging system. Inembodiments, the parameters may include a perspective of the ROI. Insome embodiments, other parameters may be used in the model.

At block 230, the processing logic determines tracking quality metricsfor perspectives that a subsequent image may be captured from. Thetracking quality metrics may indicate whether a subsequent imagecaptured from each of the perspectives will reduce the uncertainty valueof the image identified at block 210. In embodiments, the trackingquality metrics may be determined by examining higher order terms of aTaylor Series of a model fitting objective function, L, as is shownbelow:

${\hat{L} = {\sum\limits_{t}{d_{t}\left( {p_{t},{\mathcal{P}_{t}\left( {f_{s}\left( m_{t} \right)} \right)}} \right)}}}{\overset{\hat{}}{s} = {\underset{s}{\arg\min}\hat{L}}}$where:t is the time at when the measurements are made. For a rotating gantry,this time may also specify imaging system and patient support positionswith respect to a treatment beam.p_(t) are a set of tracking structure measurements or detected orcandidate positions of the tracking structure in a 2D x-ray imagem_(t) is the high-frequency surrogate motion dataf_(s) is the motion model that converts high-frequency data to theposition of the tracking structures are the parameters of the motion model

_(t) is the projection operator that relates the 3D data to itscorresponding 2D data in the x-ray image. For the case of points, thisis a perspective projection matrix. For image data, this is a DRRgenerator.d is some distance function that weights the similarity between the 2Dmeasured data p_(t) and the tracking structure in its 2D predictedlocation according to the model. In embodiments, the distance functionmay reflect the magnification of the error due to the 3D location of thetracking structure with respect to the imaging geometry. In someembodiments, the distance function may reflect a priori uncertainties inthe measurements. In an embodiment, the distance function may reflectage related down-weighting of older measurements. In embodiments, thedistance function may reflect spatial probability distribution of thecandidate tracking structure to image match, weighting the error vectorbased on the quality of the 2D image data. In some embodiments, thedistance function may reflect other factors reflecting the statisticalquality of the model as an estimate of its ability to describe themotion of the tracking structure.

The value of the model fitting objective function may be used toevaluate the model quality for the ROI, where a smaller value of theobjective function {circumflex over (L)} indicates a better model fitfor the ROI. However, this may not necessarily describe the quality ofthe model in predicting a motion associated with the ROI. For example,this may not describe how the model predicts respiratory motion of apatient associated with the ROI.

An additional measure of the quality of the model fitting may be foundby examining the shape of {circumflex over (L)} with respect to s at theminimum, where the optimal model parameters ŝ have already been found.Consider the Taylor series expansion of {circumflex over (L)} about ŝ:

${{\overset{\hat{}}{L}(s)} \approx {{\overset{\hat{}}{L}\left( \overset{\hat{}}{s} \right)} + {{\nabla{\overset{\hat{}}{L}\left( \overset{\hat{}}{s} \right)}}\left( {s - \overset{\hat{}}{s}} \right)} + {\frac{1}{2}\left( {s - \overset{\hat{}}{s}} \right)^{T}{\nabla^{2}{\overset{\hat{}}{L}\left( \overset{\hat{}}{s} \right)}}\left( {s - \overset{\hat{}}{s}} \right)} +}}\ldots$where the first term is the value of the objective function at theminimum, the second term is a gradient of the objective function, andthe third term is the Hessian matrix—the matrix of second derivatives ofthe objective function:

${\nabla^{2}{\overset{\hat{}}{L}\left( \overset{\hat{}}{s} \right)}} = {{H{\overset{\hat{}}{L}\left( \overset{\hat{}}{s} \right)}} = \frac{\partial^{2}{\overset{\hat{}}{L}\left( \overset{\hat{}}{s} \right)}}{{\partial s_{i}}{\partial s_{j}}}}$with Hessian operator H taken with respect to the parameters of themodel. This matrix may describe the curvature of the function. A largecurvature means that, given a small step in a direction s′ in modelparameter space, the value of the function changes a large amount. If,the curvature is very large in a particular direction, there is greatercertainty about the location of the minimum in that direction becausethe function/measurements are more sensitive to changes in parameters inthat direction.

When Hessians are at a minima of s′ the Hessian may be a symmetric,positive-definite (SPD) matrix. Such a matrix can be thought of asdescribing an ellipsoid (centered at zero) or quadratic field whereiso-likelihood objective function values are ellipsoidal. SPD matricesare the form of covariance matrices, and the Hessian matrix at a minimumcan be thought of as describing the covariance of the (normal)distribution from which model parameters are drawn as follows:

$\left. {{N\left( {\overset{\hat{}}{s},\Sigma_{s}} \right)} \propto e^{{- \frac{1}{2}}{({s - \overset{\hat{}}{s}})}^{T}H{\overset{\hat{}}{L}(\overset{\hat{}}{s})}{({s - \overset{\hat{}}{s}})}}}\Rightarrow e^{{- \frac{1}{2}}{({s - \overset{\hat{}}{s}})}^{T}{\Sigma_{s}^{- 1}({s - \overset{\hat{}}{s}})}} \right.$The above equation described a distribution of parameters of thegenerated model. The covariance of the distribution, Σ_(s), is theinverse of the Hessian, where the Hessian curvature matrix can bethought of as a certainty matrix and its inverse as an uncertaintymatrix, where a small uncertainty may be desirable. A model quality maybe greater if the variance of the distribution from which the model'sparameters are drawn is smaller (e.g., corresponding to a Hessian matrixthat is large). Thus, the optimal next imaging time is the time at whichthat image is capable of maximally increasing the largeness of theHessian of the objective function. Alternatively (or in addition), thenext optimal imaging time could also enforce that a minimum amount ofexpected error (given by uncertainty) is maintained.

To determine an optimal perspective for capturing an image, anotherangle may be added to the image set:

${\hat{L}}^{\prime} = {{\sum\limits_{t}{d_{t}\left( {p_{t^{\prime}}{\mathcal{P}_{t}\left( {f_{s}\left( m_{t} \right)} \right)}} \right)}} + {d_{t^{\prime}}\left( {p_{t^{\prime}},{\mathcal{P}_{t^{\prime}}\left( {f_{s}\left( a_{t^{\prime}} \right)} \right)}} \right)}}$where t′ corresponds to a time in the future when the new trackingmeasurement is to be acquired and a_(t′) corresponds to the predictedvalue of the high-frequency motion surrogate at time t′. The Hessian maybe computed with respect to the model parameters to determine thecertainty after the subsequent image:H{circumflex over (L)}′=H{circumflex over (L)}+Hd _(t′)(p _(t′),

_(t′)(f _(s)(a _(t′))))The determinant may be considered as a measure of the largeness of thecertainty matrix. The determinant of an SPD is proportional to thevolume of the ellipsoid that it represents. Therefore, the optimalimaging time may be:

$\underset{t^{\prime}}{\arg\max}{\det\left\lbrack {{H\overset{\hat{}}{L}} + {H{d_{t^{\prime}}\left( {p_{t^{\prime}},{\mathcal{P}_{t^{\prime}}\left( {f_{s}\left( a_{t^{\prime}} \right)} \right)}} \right)}}} \right\rbrack}$Reasonable extrapolation may be used to predict the unknown values attime, t′. The problem is tractable because it is a one-dimensionaloptimization over a discrete set of imaging perspectives/times. Inembodiments, this problem may be made more general by solving formultiple, future time points, For example, higher quality time pointsmay be estimated if points for a full gantry rotation are estimated atonce, taking into account any temporal limitations for the set ofpoints.

Each of the terms in the determinant may be an SPD matrix that definesan ellipsoid. The sums of the SPD matrices are SPD, resulting in thesums also representing an ellipse. The optimal time/perspective for asubsequent image may correspond to the time/perspective that produces acertainty ellipsoid that, when added to the first ellipsoid, produces anellipsoid that is the largest.

At block 240, the processing logic selects a second perspective from themultiple perspectives based on a corresponding tracking quality metricof the second perspective indicating a reduction of the uncertaintyvalue. In embodiments, the processing logic may select the secondperspective based on the corresponding tracking quality metricindicating a greatest reduction of the uncertainty value of the multipleperspectives. In an embodiment, the processing logic may select thesecond perspective based on the corresponding tracking quality metricindicating a reduction of uncertainty value that exceeds a threshold. Insome embodiments, the processing logic may select the second perspectivebased on other criteria. In embodiments, the processing logic may selectthe second perspective automatically (e.g., without user intervention)during a treatment session of a radiation delivery system.

FIG. 3A is an illustration 300 of an example of an imaging system havingmultiple perspectives of a region of interest, in accordance withembodiments of the disclosure. Illustration 300 includes imaging system302 that may be an X-ray (kV or MV) imaging system, an MR imagingsystem, or any other type of imaging system. In embodiments, the imagingsystem 302 may correspond to kV imaging source 810 of FIG. 1A or imagingsystem 1210 of FIG. 1B. In some embodiments, the imaging system 302 mayinclude an imaging detector (not shown), as previously described atFIGS. 1A-C.

In embodiments, the imaging system 302 may be coupled to a ring gantry,such as ring gantry 820 of FIG. 1A. In an embodiment, the imaging system302 may be coupled to a robotic arm, such as robotic arm 1202 of FIG.1B. In some embodiments, the imaging system 302 may be coupled to aC-arm gantry, such as C-arm gantry 1410 of FIG. 1C.

Illustration 300 further includes a region of interest 312. Inembodiments, the region of interest 312 may correspond to a region ofinterest of a patient. The region of interest 312 may include one ormore targets and/or tracking structures.

Illustration 300 includes first perspective 304, second perspective 306,third perspective 308, and fourth perspective 310 that may correspond todifferent perspectives from which an image of the region of interest 312may be captured. In embodiments, the first perspective 304 maycorrespond to the perspective from which a first image was captured, asdescribed at block 210 of FIG. 2 . In some embodiments, the secondperspective 306, third perspective 308, and fourth perspective 310 maycorrespond to a subset of the multiple perspectives considered tocapturing a subsequent image of the region of interest 312, as describedat blocks 230 and 240 of FIG. 2 .

In embodiments where imaging system 302 is coupled to a gantry, firstperspective 304, second perspective 306, third perspective 308, andfourth perspective 310 may correspond to different angles as the imagingsystem 302 relative to the region of interest 312 as the imaging system302 is rotated about the region of interest 312 by the gantry. Inembodiments where imaging system is coupled to a robotic arm, firstperspective 304, second perspective 306, this perspective 308, andfourth perspective 310 may correspond to different orientations relativeto the region of interest 312 as the imaging system 302 is positionedalong a path about the region of interest 312 by the robotic arm.

FIG. 3B is an illustration 350 of an example of selecting a perspectivefor capturing a subsequent image based on tracking quality metrics, inaccordance with embodiments of the disclosure. In illustration 350, thefirst perspective 304 corresponds to the perspective from which a firstimage has been captured. As previously described, the image may have anassociated uncertainty value (e.g., uncertainty 352).

Second perspective 306, third perspective 308, and fourth perspective310 may each have corresponding tracking quality metrics, metric 354,metric 356, and metric 358, respectively. As previously described, thetracking quality metrics may indicate a reduction in the uncertainty 352value of the image captured from the first perspective 304. Upondetermining metric 354, metric 356, and metric 358, processing logic ofa processing device may select one of the second perspective 306, thethird perspective 308, or the fourth perspective 310 for capturing asubsequent image of the region of interest 312.

Referring to FIG. 3B, metric 354 has a value of 2.4, metric 356 has avalue of 8.2, and metric 358 has a value of 6.3. Because metric 356 hasthe largest value, metric 356 indicates that a subsequent image capturedfrom the third perspective 308 may have a greatest reduction in theuncertainty 352 value of the first image relative to the secondperspective 306 and the fourth perspective 310, which have lowercorresponding metrics (e.g., metric 354 and metric 358). Accordingly,the processing logic may select the third perspective 308 for capturinga subsequent image.

In embodiments, a time may be selected by the processing logic for thecapturing of the subsequent image from the third perspective 308. Forexample, if the region of interest 312 is a location that may beaffected by the respiratory motion of a patient, the particular time forcapturing the subsequent image from the third perspective may coincidewith a particular respiratory phase of the patient to maximize thereduction in position uncertainty.

Although FIGS. 3A and 3B illustrate four perspectives for capturing asubsequent image, embodiments of the disclosure may include any numberof perspectives that may be considered for the capturing of thesubsequent image. Furthermore, although FIG. 3B described selecting thethird perspective 308 based on a greatest reduction in uncertainty,embodiments of the disclosure may utilize other criteria for selecting aperspective for capturing the subsequent image.

FIG. 4 depicts a flow diagram of a method 400 of updating a model basedon receiving a subsequent image of a region of interest, in accordancewith embodiments of the disclosure. Method 400 may be performed byprocessing logic that may comprise hardware (e.g., circuitry, dedicatedlogic, programmable logic, a processor, a processing device, a centralprocessing unit (CPU), a system-on-chip (SoC), etc.), software (e.g.,instructions running/executing on a processing device), firmware (e.g.,microcode), or a combination thereof. In embodiments, various portionsof method 400 may be performed by processing logic of a processingdevice of a radiation delivery system as previously described at FIGS.1A-1C.

With reference to FIG. 4 , method 400 illustrates example functions usedby various embodiments. Although specific function blocks (“blocks”) aredisclosed in method 400, such blocks are examples. That is, embodimentsare well suited to performing various other blocks or variations of theblocks recited in method 400. It is appreciated that the blocks inmethod 400 may be performed in an order different than presented, andthat not all of the blocks in method 400 may be performed.

Method 400 begins at block 410, where the processing logic causes animaging system to capture a subsequent image of an ROI. The subsequentimage may be captured from a perspective that is selected based on areduction of an uncertainty value associated with a first image, aspreviously described at FIG. 2 . In embodiments, the subsequent imagemay be captured during a treatment session being performed by aradiation delivery system.

At block 420, the processing logic receives the subsequent image fromthe imaging system.

At block 430, the processing logic updates a model including one or moreparameters associated with the ROI based on the subsequent image. Inembodiments, the model may correspond to the model generated at block220 of FIG. 2 . In some embodiments, updating the model may includemodifying, adding, and/or removing parameters of the model. For example,upon receiving the subsequent image that includes additional informationassociated with a position of a radiation sensitive structure within theROI, the processing logic may modify the parameter of the model thatcorresponds to the position of the radiation sensitive structure withinthe ROI.

FIG. 5 is a block diagram of an example computing device 500 that mayperform one or more of the operations described herein, in accordancewith some embodiments. Computing device 500 may be connected to othercomputing devices in a LAN, an intranet, an extranet, and/or theInternet. The computing device may operate in the capacity of a servermachine in client-server network environment or in the capacity of aclient in a peer-to-peer network environment. The computing device maybe provided by a personal computer (PC), a set-top box (STB), a server,a network router, switch or bridge, or any machine capable of executinga set of instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single computing deviceis illustrated, the term “computing device” shall also be taken toinclude any collection of computing devices that individually or jointlyexecute a set (or multiple sets) of instructions to perform the methodsdiscussed herein.

The example computing device 500 may include a processing device (e.g.,a general purpose processor, a PLD, etc.) 502, a main memory 504 (e.g.,synchronous dynamic random access memory (DRAM), read-only memory(ROM)), a static memory 506 (e.g., flash memory and a data storagedevice 518), which may communicate with each other via a bus 530.

Processing device 502 may be provided by one or more general-purposeprocessing devices such as a microprocessor, central processing unit, orthe like. In an illustrative example, processing device 502 may comprisea complex instruction set computing (CISC) microprocessor, reducedinstruction set computing (RISC) microprocessor, very long instructionword (VLIW) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. Processing device 502 may also comprise one or morespecial-purpose processing devices such as an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Theprocessing device 502 may be configured to execute the operationsdescribed herein, in accordance with one or more aspects of the presentdisclosure, for performing the operations and steps discussed herein.

Computing device 500 may further include a network interface device 508which may communicate with a network 520. The computing device 500 alsomay include a video display unit 510 (e.g., a liquid crystal display(LCD) or a cathode ray tube (CRT)), an alphanumeric input device 512(e.g., a keyboard), a cursor control device 514 (e.g., a mouse) and anacoustic signal generation device 516 (e.g., a speaker). In oneembodiment, video display unit 510, alphanumeric input device 512, andcursor control device 514 may be combined into a single component ordevice (e.g., an LCD touch screen).

Data storage device 518 may include a computer-readable storage medium528 on which may be stored one or more sets of instructions that mayinclude perspective selection instructions 525 for carrying out theoperations described herein, in accordance with one or more aspects ofthe present disclosure. The instructions may also reside, completely orat least partially, within main memory 504 and/or within processingdevice 502 during execution thereof by computing device 500, main memory504 and processing device 502 also constituting computer-readable media.The instructions may further be transmitted or received over a network520 via network interface device 508.

While computer-readable storage medium 528 is shown in an illustrativeexample to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database and/or associated cachesand servers) that store the one or more sets of instructions. The term“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform the methods described herein. The term “computer-readablestorage medium” shall accordingly be taken to include, but not belimited to, solid-state memories, optical media and magnetic media.

It should be noted that the methods and apparatus described herein arenot limited to use only with medical diagnostic imaging and treatment.In alternative implementations, the methods and apparatus herein may beused in applications outside of the medical technology field, such asindustrial imaging and non-destructive testing of materials. In suchapplications, for example, “treatment” may refer generally to theeffectuation of an operation controlled by the treatment planningsystem, such as the application of a beam (e.g., radiation, acoustic,etc.) and “target” may refer to a non-anatomical object or area.

The preceding description sets forth numerous specific details such asexamples of specific systems, components, methods, and so forth, inorder to provide a good understanding of several embodiments of thepresent disclosure. It will be apparent to one skilled in the art,however, that at least some embodiments of the present disclosure may bepracticed without these specific details. In other instances, well-knowncomponents or methods are not described in detail or are presented insimple block diagram format in order to avoid unnecessarily obscuringthe present disclosure. Thus, the specific details set forth are merelyexemplary. Particular embodiments may vary from these exemplary detailsand still be contemplated to be within the scope of the presentdisclosure.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiments included inat least one embodiment. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment” in various places throughout thisspecification are not necessarily all referring to the same embodiment.

Although the operations of the methods herein are shown and described ina particular order, the order of the operations of each method may bealtered so that certain operations may be performed in an inverse orderor so that certain operation may be performed, at least in part,concurrently with other operations. In another embodiment, instructionsor sub-operations of distinct operations may be in an intermittent oralternating manner.

The above description of illustrated implementations of the invention,including what is described in the Abstract, is not intended to beexhaustive or to limit the invention to the precise forms disclosed.While specific implementations of, and examples for, the invention aredescribed herein for illustrative purposes, various equivalentmodifications are possible within the scope of the invention, as thoseskilled in the relevant art will recognize. The words “example” or“exemplary” are used herein to mean serving as an example, instance, orillustration. Any aspect or design described herein as “example” or“exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the words“example” or “exemplary” is intended to present concepts in a concretefashion. As used in this application, the term “or” is intended to meanan inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Moreover, use of the term “an embodiment” or “one embodiment” or“an implementation” or “one implementation” throughout is not intendedto mean the same embodiment or implementation unless described as such.Furthermore, the terms “first,” “second,” “third,” “fourth,” etc. asused herein are meant as labels to distinguish among different elementsand may not necessarily have an ordinal meaning according to theirnumerical designation.

What is claimed is:
 1. An apparatus, comprising: an imaging system; anda processing device, operatively coupled to the imaging system, to:select at least one of a plurality of optimal imaging times or aplurality of imaging angles; and optimize a parameter of the imagingsystem based on the at least one of the plurality of optimal imagingtimes or the plurality of imaging angles.
 2. The apparatus of claim 1,wherein the parameter comprises an imaging perspective of a region ofinterest.
 3. The apparatus of claim 1, the parameter comprises aposition, an orientation, or a timing of images that are acquired of aregion of interest (ROI).
 4. The apparatus of claim 1, wherein theimaging system comprises an X-ray imaging system.
 5. The apparatus ofclaim 1, wherein to optimize, the processing device is to reduce anuncertainty value associated with a region of interest (ROI).
 6. Theapparatus of claim 1, wherein optimizing comprises maximizing avisibility of structures in a region of interest (ROI).
 7. The apparatusof claim 1, wherein the imaging system comprises a magnetic resonance(MR) imaging system.
 8. A method comprising: selecting of at least oneof a plurality of optimal imaging times or a plurality of imagingangles; and optimizing a parameter of an imaging system based on the atleast one of the plurality of optimal imaging times or the plurality ofimaging angles.
 9. The method of claim 8, wherein the parametercomprises an imaging perspective of a region of interest (ROI).
 10. Themethod of claim 8, the parameter comprises a position, an orientation,or a timing of images that are acquired of a region of interest (ROI).11. The method of claim 8, wherein the imaging system comprises an X-rayimaging system.
 12. The method of claim 8, wherein optimizing comprisesreducing an uncertainty value associated with a region of interest(ROI).
 13. The method of claim 8, wherein optimizing comprisesmaximizing a visibility of structures in a region of interest (ROI). 14.The method of claim 8, wherein the imaging system comprises a magneticresonance (MR) imaging system.
 15. A non-transitory computer-readablestorage medium including instructions that, when executed by aprocessing device, cause the processing device to: select at least oneof a plurality of optimal imaging times or a plurality of imagingangles; and optimize a parameter of an imaging system based on the atleast one of the plurality of optimal imaging times or the plurality ofimaging angles.
 16. The non-transitory computer-readable storage mediumof claim 15, wherein the parameter comprises a position, an orientation,or a timing of images that are acquired of a region of interest (ROI).17. The non-transitory computer-readable storage medium of claim 15,wherein the imaging system comprises an X-ray imaging system.
 18. Thenon-transitory computer-readable storage medium of claim 15, wherein tooptimize, the processing device is to reduce an uncertainty valueassociated with a region of interest (ROI).
 19. The non-transitorycomputer-readable storage medium of claim 15, wherein optimizingcomprises maximizing a visibility of structures in a region of interest(ROI).
 20. The non-transitory computer-readable storage medium of claim15, wherein the imaging system comprises a magnetic resonance (MR)imaging system.
 21. An apparatus, comprising: an imaging system; and aprocessing device, operatively coupled to the imaging system, to: selectat least one of a plurality of image acquisition parameters; andoptimize an imaging system parameter of the imaging system based on theat least one of the plurality of image acquisition parameters.
 22. Theapparatus of claim 21, wherein the imaging system comprises an X-rayimaging system.
 23. The apparatus of claim 21, wherein to optimize, theprocessing device is to reduce an uncertainty value associated with aregion of interest (ROI).
 24. The apparatus of claim 21, whereinoptimizing comprises maximizing a visibility of structures in a regionof interest (ROI).
 25. The apparatus of claim 21, wherein the imagingsystem comprises a magnetic resonance (MR) imaging system.